MERL Mechatronics Senior Principal Research Scientist and Senior Optimization-based Control Team Leader, Stefano Di Cairano, was recently appointed the Vice-Chair of IFAC (International Federation of Automatic Control) Technical Committee for Optimal Control. His term will continue through July 2020.

The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.

Stefano Di Cairano has become Senior Member of IEEE. In addition, he has been asked by the Vice President for Technical Activities of the Control System Society (CSS) of IEEE to take the role of Chair of the Standing Committee on Standards. S. Di Cairano will succeed Dr. T. Samad, Honeywell, as chair of the committee. His nomination should be ratified by the IEEE-CSS Board of Governor at the meeting in Osaka, in December 2015.

MERL researchers presented 10 papers at the American Controls Conference, in Chicago, USA. The ACC is one of the most important conferences on control systems in the world. Topics ranged from theoretical, including new algorithms for Model Predictive Control and Co-Design, to applications including spacecraft control and HVAC systems.

Although there are many fault diagnosis algorithms available, there has been very little work on the design or modification of control inputs with the aim of increasing the detectability and isolability of faults. The use of such inputs has clear potential for overcoming a central difficulty in fault detection, which is to distinguish the effects of faults from those of disturbances, process uncertainties, etc. Accordingly, the use of active inputs could be a transformative technology in industry, provided that such inputs can be computed reliably and efficiently.
This presentation discusses new methods for computing active inputs that guarantee that the input-output data of a process will be sufficient to correctly identify a fault from a given library of possible faults. This problem is inherently nonconvex and has a combinatorial dependence on the number of faults considered. To address this, a new formulation is considered, along with related approximations, that is amenable to efficient solution using standard optimization packages (e.g. CPLEX). The theoretical contributions combine ideas from reachability analysis, set-based computations, and optimization theory to exploit detailed problem structure and thereby manage the problem complexity. Comparisons with an existing method show that the proposed formulation provides a dramatic reduction in the required computational effort.

This talk will present the breadth of research activities in the Intelligent Systems, Automation & Control Laboratory at Rensselaer Polytechnic Institute, ranging from building systems control to additive manufacturing and adaptive optics. In particular, we will focus on the modeling and control design paradigms for intelligent building systems and smart LED lighting systems. Since building systems have substantial variability of occupancy, usage, ambient environment, and physical properties over time, strategies for "model-free" control algorithms for building temperature control will be illustrated. The seminar will also discuss the state-of-the-art in feedback control of lighting systems and demonstrate the efficacy of distributed control and consensus type algorithms for these large-scale lighting systems. Finally, some interesting examples of bio-inspired estimation from blurry images for adaptive optics will be presented.

The article "Vehicle Yaw Stability Control by Coordinated Active Front Steering and Differential Braking in the Tire Sideslip Angles Domain" by Di Cairano, S., Tseng, H.E., Bernardini, D. and Bemporad, A. was published in IEEE Transactions on Control Systems Technology

The paper "An Industry Perspective on MPC in Large Volumes Applications: Potential Benefits and Open Challenges" by Di Cairano, S. was presented at the IFAC Nonlinear Model Predictive Control Conference (NMPC)

The article "Model Predictive Control Approach for Guidance of Spacecraft Rendezvous and Proximity Maneuvering" by Di Cairano, S., Park, H. and Kolmanovsky, I. was published in International Journal of Robust and Nonlinear Control

Operator error is a significant factor in a majority of manned and unmanned vehicle accidents. In this talk, a framework for semi-autonomous vehicle accident avoidance will be presented that has been shown to effectively mitigate collisions caused by operator error. The framework analyzes sensor data (from vision and/or LIDAR data) to identify "no go" regions in the environment, and automatically synthesize constraints on vehicle position. An optimal trajectory and associated control inputs are then found via linear or nonlinear model predictive control. The "threat" to the vehicle is quantified from various metrics computed over the optimal trajectory. A number of approaches for arbitrating between operator and control system authority, based on the predicted threat, will be discussed. Extensive simulation and experimental testing will be described for both manned and unmanned scenarios. Future directions in threat assessment and semi-autonomous control, based on the integration of vision-based sensing and active steering control, will also be discussed.

The papers "Further Developments and Applications of Network Reference Governor for Constrained Systems" by Di Cairano, S. and Kolmanovsky, I.V. and "Load Positioning in the Presence of Base Vibrations" by Shilpiekandula, V., Bortoff, S.A., Barnwell, J.C. and El Rifai, K. were presented at the American Control Conference (ACC)

In this talk, a real-time algorithm for nonlinear model predictive control and its applications will be introduced. The continuation method is combined with an efficient linear solver GMRES to trace the time-dependent optimal solution without iterative searches. Applications of the algorithm include position control of an underactuated hovercraft, route tracking of a ship with redundant actuators, and path generation for an automobile. Automatic code generation by symbolic computation and other related topics will also be introduced.

Forecasts will play an increasingly important role in the next generation of autonomous and semi-autonomous systems. In nominal conditions, predictions of system dynamics, human behavior and environmental envelope can be used by the control algorithm to improve safety and performance of the resulting system. However, in practice, constraint satisfaction, performance guarantees and real-time computation are challenged by the (1) growing complexity of the engineered system, (2) uncertainty in the human/machine interaction and (3) uncertainty in the environment where the system operates.

In this talk I will present the theory and tools that we have developed over the past ten years for the systematic design of predictive controllers for uncertain linear and nonlinear systems. I will first provide an overview of our theoretical efforts. Then, I will focus on our recent results in addressing constraint satisfaction and real-time computation in nonlinear systems and large-scale networked systems. Throughout the talk I will use two applications to motivate our research and show the benefits of the proposed techniques: Safe Autonomous Cars and Green Intelligent Buildings.